The present disclosure relates generally to systems and methods for magnetic resonance imaging (“MRI”). More particularly, the present disclosure relates to systems and methods for producing quantitative parameter maps using magnetic resonance fingerprinting (“MRF”).
MRF is an imaging technique that enables quantitative mapping of tissue or other material properties based on random or pseudorandom measurements of the subject or object being imaged. Examples of parameters that can be mapped include longitudinal relaxation time, T1; transverse relaxation time, T2; main magnetic field map, B0; and proton density, ρ. MRF is generally described in U.S. Pat. No. 8,723,518, which is herein incorporated by reference in its entirety.
The random or pseudorandom measurements obtained in MRF techniques are achieved by varying the acquisition parameters from one repetition time (“TR”) period to the next, which creates a time series of images with varying contrast. Examples of acquisition parameters that can be varied include flip angle, radio frequency (“RF”) pulse phase, TR, echo time (“TE”), and sampling patterns, such as by modifying one or more readout encoding gradients.
MR Fingerprinting requires matching an acquired magnetization signal to a pre-computed dictionary. More particularly, the data acquired with MRF techniques are compared with a dictionary of signal models, or templates, that have been generated for different acquisition parameters from magnetic resonance signal models, such as Bloch equation-based physics simulations. This comparison allows estimation of the desired physical parameters, such as those mentioned above. The parameters for the tissue or other material in a given voxel are estimated to be the values that provide the best signal template matching.
To be effective, the dictionary must adequately cover the range of tissue parameters and values to obtain an accurate matching. However, the dictionary size needed rapidly increases with the number of tissue maps desired. The larger dictionaries require significant memory, storage, and processing time. Some, such as Cauley, Stephen F., et al. “Fast group matching for MR fingerprinting reconstruction.” Magnetic Resonance in Medicine (2014), have attempted to address this issue by pre-grouping the dictionary entries and using the singular value decomposition (SVD) to truncate the matrix. Unfortunately, for large dictionaries, the grouping and SVD may themselves be resource intensive. Additionally, the truncation applied in this process risks eliminating signals needed for accurate matching.
Therefore, a need persists for a fast matching method that does not truncate any dictionary entries or require significant computational resources.